Human language is ambiguous. Whether in the English language, French language, Russian language or many other human languages, many words may be interpreted in multiple ways depending on the context in which they occur. Such words may be referred to as homonym words (also referred to as “homonym(s)”). As an example, the word “bank” in the English language may be associated with different meanings. As a first example, “if you want to buy a bicycle, you need to put the money in your piggy bank”. As a second example, “the pilot must know how to bank the aircraft”. In the first example, the word “bank” may be associated with a lexical tag “noun” and with a first meaning corresponding to a device used to store coins. In the second example, the word “bank” may be associated with a lexical tag “verb” and with a second meaning corresponding to causing an aircraft to modify its incline. Distinguishing the first meaning from the second meaning may be, in appearance, straight forward for human beings understanding the different contexts in which the word “bank” is used. While, most of the time, human beings may not even think about ambiguities which may be found in human languages, computer-implemented systems need to process unstructured textual information and transform it into data structures which may be analysed in order to determine an underlying meaning. Identification of meanings for words by computer-implemented systems is often referred to as Word Sense Disambiguation (or also referred to as “WSD”).
WSD may be used in a multitude of contexts involving processing of text by computer-implemented systems. Such contexts may involve, for example, search engines, automatic translation, automatic training, content extraction and/or learning by computer-implemented systems.